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Colloquium on Digital Transformation Science

  • October 28, 3 pm CT

    Deep Learning to Replace, Improve, or Aid CFD analysis in Built Environment Applications

    Wei Liu, Assistant Professor of Civil and Architectural Engineering, KTH Royal Institute of Technology


    Fast and accurate airflow simulations in the built environment are critical to provide acceptable thermal comfort and air quality to occupants. Computational Fluid Dynamics (CFD) offers detailed analysis on airflow motion, heat transfer, and contaminant transport in indoor environments, as well as wind flow and pollution dispersion around buildings in urban environments. However, CFD still faces many challenges, mainly in terms of computational expense and accuracy. With the increasing availability of large amounts of data, data-driven models are starting to be investigated to either replace, improve, or aid CFD simulations. More specifically, the abilities of deep learning and Artificial Neural Networks (ANN) as universal non-linear approximators, handling of high dimensionality fields, and lower computational expense are very appealing. In built environment research, deep learning applications to airflow simulations show the ANN as a surrogate, replacement for expensive CFD analysis. Surrogate modeling enables fast or even real-time predictions, but usually at the cost of degraded accuracy. This talk presents the deep learning interactions with fluid mechanics simulations in general and proposes different techniques other than surrogate modeling for built environment applications. There are promising methods largely yet to be explored in the built environment scene.

    Wei Liu is an assistant professor at the Division of Sustainable Buildings, Department of Civil and Architectural Engineering, KTH Royal Institute of Technology in Stockholm, Sweden. Liu's current research topics include Indoor Air Quality and Air Distribution, Inverse Design and Control of indoor environments, and Data-Driven/AI-based Smart Buildings. He has published 47 journal papers and 30 conference papers. Liu is an Outstanding Winner and recipient of INFORMS Award from the Mathematical Contest in Modeling 2019, Best Paper Award from ROOMVENT 2018, Bilsland Dissertation Fellowship from Purdue University in 2016, and First Prize of the RP-1493-Shootout Contest from ASHRAE in 2012.

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